Quantitative monitoring method for energy consumption of electric automobile

Abstract

The invention discloses an energy consumption quantitative monitoring method for an electric vehicle. The monitoring method comprises the following steps: dividing battery aging phase states of a vehicle-mounted battery according to an SOH value of the vehicle-mounted battery; dividing a complete travel of the electric vehicle into a plurality of driving segments with a certain duration, taking the driving segments as units, and extracting vehicle driving state characteristic parameters of the driving segments from the driving segments; performing clustering analysis according to the vehicle driving state characteristic parameters to obtain a plurality of different driving modes; a battery aging phase state-driving mode coupling matrix is established, each element in the coupling matrix corresponds to different combinations of battery aging phase states and driving modes, and a corresponding energy consumption equation is established based on each combination; and determining a combination corresponding to each driving segment, calculating an energy consumption result of each driving segment according to the energy consumption equation corresponding to each combination, and generating an energy consumption result of the complete journey from the energy consumption results of the plurality of driving segments by adopting a weighting mode.

Description

Technical Field

[0001] This invention relates to the field of energy consumption monitoring technology, and in particular to a method for quantitative monitoring of energy consumption of electric vehicles. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the quantitative monitoring of electric vehicle energy consumption has become one of the core technologies of concern in the industry. It not only directly relates to users' prediction of vehicle range and control of operating costs, but is also a key basis for automakers to optimize vehicle powertrain design, improve battery management strategies, and meet industry energy consumption standards. Currently, electric vehicle energy consumption monitoring methods mainly revolve around bench test calibration, real-vehicle data fitting, and single-parameter correlation modeling. By establishing a mapping relationship between energy consumption and parameters such as vehicle speed, acceleration, and battery SOC, energy consumption can be estimated and monitored.

[0003] However, existing technologies still have many technical shortcomings that urgently need to be addressed in practical applications, making it difficult to meet market demands in terms of energy consumption monitoring accuracy and practicality. Firstly, existing energy consumption monitoring models mostly employ fixed parameters or single-dimensional correction methods, which do not comprehensively consider the battery aging state and lack precision. Most methods simply refer to the single parameter of battery state of health (SOH), without considering key factors such as effective battery usage time and cumulative cycle count. This fails to accurately reflect the changes in the battery's electrochemical characteristics throughout its entire lifespan, leading to significant deviations in energy consumption calculations for aged batteries. This is especially true after the battery enters its degradation phase, when the nonlinear changes in energy consumption caused by increased internal resistance and decreased charge / discharge efficiency are difficult to quantify effectively. Summary of the Invention

[0004] In view of the above-mentioned prior art, the present invention provides a method for quantitative monitoring of energy consumption of electric vehicles, which mainly solves the technical problems existing in the background art.

[0005] To achieve the above objectives, the technical solution of this invention is implemented as follows: This invention discloses a method for quantitative monitoring of energy consumption of electric vehicles, the monitoring method comprising the following steps: The battery aging phase is classified according to the SOH value of the vehicle battery; A complete journey of an electric vehicle is divided into multiple driving segments with a certain duration. The vehicle driving state characteristic parameters of each driving segment are extracted from the segments. Cluster analysis is performed based on vehicle driving status characteristic parameters to obtain multiple different driving modes; Establish a battery aging phase-driving mode coupling matrix. Each element in the coupling matrix corresponds to a different combination of battery aging phase and driving mode, and establish a corresponding energy consumption equation based on each combination. The combination corresponding to each driving segment is determined, and the energy consumption result of each driving segment is calculated according to the energy consumption equation corresponding to each combination. The energy consumption results of multiple driving segments are weighted to generate the energy consumption result of the complete journey.

[0006] Preferably, the SOH value is compared with multiple predetermined aging phase threshold ranges to determine the current aging phase of the battery, wherein the aging phase includes multiple different aging stages based on the battery health status.

[0007] Preferably, the vehicle driving state characteristic parameters include speed standard deviation, mean acceleration, Shannon entropy, and frequency of acceleration / rapid deceleration events.

[0008] Preferably, the complete journey is continuously divided into multiple time intervals using a preset fixed time length as the unit, wherein each time interval corresponds to one of the driving segments, and the driving segments are continuous in time and do not overlap.

[0009] Preferably, cluster analysis is performed based on vehicle driving state characteristic parameters to obtain multiple different driving modes. Specifically, this includes: generating corresponding feature vectors for all vehicle driving state characteristic parameters; using the distance between feature vectors as a similarity metric for cluster analysis; employing the XMeans algorithm, which can automatically discover the number of clusters; and using the Bayesian information criterion as a guide during the cluster analysis process. The cluster centers of different clusters represent different driving modes. Multiple different driving modes are obtained through clustering, where each driving mode represents a driving state with the same energy consumption level.

[0010] Preferably, a battery aging phase-driving mode coupling matrix is ​​established, specifically including: constructing a two-dimensional matrix group with multiple aging phases as the first dimension and multiple driving modes as the second dimension, wherein each matrix element in the matrix is ​​determined by the correspondence between a specific aging phase and a specific driving mode.

[0011] Preferably, for each cell in the matrix that is determined by a specific aging phase and a specific driving mode, an energy consumption equation is independently established. Some parameters in the energy consumption equation are determined by fitting the operating data collected from real vehicle tests using regression analysis, while the remaining parameters are calculated based on the SOH value and the average speed of the segment.

[0012] Preferably, the combination corresponding to each driving segment is determined, specifically including: Based on the battery health status data in the driving segment, determine the battery aging phase to which it belongs; The standardized feature vector composed of the vehicle driving state feature parameters of the driving segment is used to calculate the distance with all the determined driving mode cluster centers, and the segment is classified into the driving mode corresponding to the cluster center with the smallest distance. The determined battery aging phase and driving mode are used as a combined index and mapped to the corresponding matrix elements in the coupling matrix.

[0013] Preferably, the corresponding values ​​in the driving segment are substituted into the corresponding energy consumption equation to obtain the energy consumption calculation result corresponding to the driving segment, and the energy consumption calculation results of multiple driving segments are weighted to generate the energy consumption result of the complete journey.

[0014] Preferably, the proportion of the driving distance corresponding to each driving segment to the total distance of the complete journey is used as the weight coefficient of that segment. The energy consumption results of each segment are multiplied by their corresponding weight coefficients and then summed to obtain the energy consumption results of the complete journey.

[0015] The beneficial effects of this invention are as follows: By using a clustering algorithm, based on core feature parameters such as speed standard deviation, mean acceleration, Shannon entropy, and frequency of acceleration / rapid deceleration events, the optimal number of driving modes is automatically discovered and classified, ensuring that each driving mode can accurately correspond to a driving state with the same energy consumption level. On this basis, a dedicated energy consumption equation is established for each combination through the battery aging phase-driving mode coupling matrix, deeply coupling the nonlinear effects of battery aging with the energy consumption characteristics of the driving mode, so that energy consumption calculation can simultaneously adapt to the battery's entire life cycle state and diverse driving scenarios. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a method for quantitative monitoring of energy consumption of an electric vehicle according to an embodiment of this application. Detailed Implementation

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. In the following description, the expression "some embodiments" refers to a subset of all possible embodiments; however, it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict.

[0019] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0020] It should be understood that the present invention can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Furthermore, the terminology used herein is intended only to describe particular embodiments and is not intended to limit the invention. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “compose” and / or “comprising,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0021] It should also be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "inner," "outer," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0022] To fully understand this invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by this invention. Optional embodiments of the invention are described in detail below; however, in addition to these detailed descriptions, the invention may have other embodiments.

[0023] Please refer to the attached document. Figure 1 This application provides a method for quantitative monitoring of the energy consumption of electric vehicles, the monitoring method comprising the following steps: S1. Classify the battery aging phases of the vehicle battery according to the SOH value; As an example, the aging phase of an onboard battery is determined based on an aging comprehensive coefficient, which is obtained by comprehensively assessing the battery's State of Health (SOH), effective usage time, and total cycle count. The SOH, effective usage time, and total cycle count are all obtained by the onboard MCU from the Battery Management System (BMS). The SOH is calculated by comparing the battery's current usable capacity, which is detected in real time by the BMS, with the manufacturer's rated capacity. The effective usage time (T) is measured in years and is defined as the cumulative active charging and discharging time from the battery's first charging activation. The onboard timing module automatically excludes idle periods exceeding 7 days and only counts the actual duration of charge and discharge cycles. The total cycle count is automatically counted by the BMS, with each complete discharge to SOC ≤ 20% and charging to SOC ≥ 80% considered as one complete cycle.

[0024] By fitting a large amount of power battery life cycle test data, a quantitative calculation formula for the aging comprehensive coefficient was obtained:

[0025] Where K represents the aging comprehensive coefficient, T represents the effective service time, and N represents the total number of cycles.

[0026] Furthermore, the K value is compared with multiple predetermined aging phase threshold ranges to determine the current aging phase of the battery. The aging phase includes multiple different aging stages based on the battery's health status. Specifically, the corresponding threshold between the K value and the aging phase is determined by fitting experimental data. When K ≥ 0.85, the battery is determined to be in the initial stage; when 0.70 ≤ K < 0.85, it is determined to be in the transition stage; when 0.55 ≤ K < 0.70, it is determined to be in the degradation stage; and when K < 0.55, it is determined to be in the steep decline stage.

[0027] S2. Divide a complete journey of an electric vehicle into multiple driving segments with a certain duration, and extract the vehicle driving state characteristic parameters of the driving segment from each driving segment. As an example, the definition of a complete trip is to start the vehicle's power system and end the power system, forming a complete driving cycle. Regardless of whether there are brief stops, idling, or other states during the trip, they are all included in the statistical scope of this trip to ensure the completeness of the trip data.

[0028] Furthermore, the complete journey is continuously divided into multiple time intervals using a preset fixed time length as the unit, where each time interval corresponds to a driving segment, and the driving segments are continuous in time and do not overlap.

[0029] Specifically, a fixed-duration sliding window method is used for segmentation. Considering both operational stability and computational efficiency, the duration of each driving segment is set to 1 minute. This duration ensures a certain continuity of the driving state within a single segment while avoiding problems such as mixed operational conditions and blurred features due to excessively long durations, or data redundancy and a surge in computational load due to excessively short durations. During the segmentation process, the starting point of the journey is used as the zero point, and segments are sequentially extracted from 0-60 seconds, 60-120 seconds, and so on, until the end of the journey. For segments with a duration of less than 1 minute at the end, if their duration is ≥30 seconds, they are retained as a complete segment; if their duration is <30 seconds, they are merged with the previous segment, ensuring that each segment has effective analytical value. Meanwhile, invalid data segments with continuous idling time exceeding 30 seconds are automatically removed. When the vehicle speed is ≤0.5km / h and the motor output power is ≤500W, it is determined to be in an idling state. If the effective driving time of the segment is less than 20 seconds after removal, it is merged with the adjacent segment to avoid interference from invalid data on feature extraction.

[0030] It should be noted that the 1-minute driving segment is only one preferred implementation method. In actual implementation, the duration can be set freely and is not limited to only 1 minute.

[0031] Furthermore, the vehicle driving state characteristic parameters include speed standard deviation, mean acceleration, Shannon entropy, and frequency of acceleration / rapid deceleration events.

[0032] S3. Perform cluster analysis based on vehicle driving status characteristic parameters to obtain multiple different driving modes; As an example, cluster analysis is performed based on vehicle driving state characteristic parameters to obtain multiple different driving modes. Specifically, this includes generating corresponding feature vectors from all vehicle driving state characteristic parameters for preprocessing. For instance, the Z-score standardization method is used to eliminate the influence of dimensions; that is, by calculating the mean and standard deviation of each parameter, the original data is transformed into standardized data with a mean of 0 and a standard deviation of 1. After preprocessing, each driving segment is treated as an independent sample. The four parameters—standardized speed standard deviation, mean acceleration, Shannon entropy, and frequency of acceleration / deceleration events—are combined in a fixed order to generate a four-dimensional feature vector for each sample. For example, the feature vector of a certain driving segment can be represented as [standardized speed standard deviation, standardized mean acceleration, standardized Shannon entropy, standardized frequency of acceleration / deceleration events]. The feature vectors of all driving segments together constitute the sample dataset for cluster analysis.

[0033] During cluster analysis, the XMeans algorithm, which can automatically determine the number of clusters, is used. This algorithm does not require manual pre-setting of the number of driving modes and can adaptively determine the optimal number of clusters based on the distribution characteristics of the data itself. The similarity metric uses the Euclidean distance between feature vectors, which is calculated by taking the square root of the sum of the squares of the differences in corresponding dimensional parameters between the feature vectors of two samples. The smaller the Euclidean distance, the more similar the driving state characteristics and the closer their energy consumption characteristics are; conversely, the greater the difference.

[0034] In the algorithm implementation, a small number of clusters is initialized to perform preliminary clustering on the sample dataset, obtaining initial clustering results. Then, for each initial cluster, its Bayesian Information Criterion (BIC) value is calculated. The BIC value comprehensively considers the goodness of fit and model complexity of the clustering model; the smaller the value, the better the model fits the data. Next, each cluster is split into two sub-clusters, and the BIC values ​​before and after the split are calculated separately. If the total BIC value after the split is less than that before the split, it means that the split can improve the model's fit, so the split result is retained; otherwise, the split is not performed. The above split evaluation process is repeated until all clusters cannot reduce the BIC value through splitting. The number of clusters obtained at this point is the number of optimal driving modes, and each cluster corresponds to an independent driving mode.

[0035] Each driving mode obtained through clustering is characterized by the feature vector of its cluster center, representing four parameters: speed standard deviation, mean acceleration, Shannon entropy, and frequency of acceleration / deceleration events. These parameters directly reflect the typical characteristics of the driving state under that mode. For example, a cluster center with a small speed standard deviation, a mean acceleration close to 0, a low Shannon entropy, and few acceleration / deceleration events can be considered a smooth driving mode with relatively low energy consumption. Conversely, a cluster center with a large speed standard deviation, a large absolute value of the mean acceleration, a high Shannon entropy, and many acceleration / deceleration events can be considered an aggressive driving mode with relatively high energy consumption. Each driving mode represents a class of driving states with the same energy consumption level.

[0036] S4. Establish a battery aging phase-driving mode coupling matrix. Each element in the coupling matrix corresponds to a combination of different battery aging phases and driving modes. Based on each combination, establish the corresponding energy consumption equation. In one example, a battery aging phase-driving mode coupling matrix is ​​established, specifically including: multiple aging phases as the first dimension, namely, the initial stage, transition stage, degradation stage, and steep drop stage objectively divided by the aging comprehensive coefficient K, each phase representing a group of battery states with similar electrochemical characteristics; and multiple driving modes as the second dimension, namely, various driving modes obtained by XMeans clustering analysis, each mode corresponding to a group of driving states with similar driving characteristics and energy consumption levels. Based on the above two dimensions, a two-dimensional matrix group is constructed. Each matrix element in the matrix is ​​determined by the correspondence between a specific aging phase and a specific driving mode. The form of each element in the matrix is, for example, initial stage - smooth driving mode, initial stage - gentle acceleration driving mode, and so on, to achieve comprehensive coverage of all possible combinations.

[0037] Furthermore, for each cell in the matrix that is determined by a specific aging phase and a specific driving mode, an energy consumption equation is independently established. Some parameters in the energy consumption equation are determined by fitting the operating data collected from real vehicle tests using regression analysis, while the remaining parameters are calculated based on the SOH value and the average speed of the segment.

[0038] The general expression for the energy consumption equation is:

[0039] in This represents the dynamic equivalent baseline energy consumption under different aging phases. It is used to represent the theoretical minimum energy consumption baseline under the current battery aging state, and its calculation formula is as follows:

[0040] in This indicates the original rated energy consumption under standard operating conditions (CLTC / NEDC) obtained through bench testing at the time of manufacture. The first parameter is a pre-calibrated parameter for different aging phases.

[0041] This represents the aging impact coefficient, reflecting the amplification effect of aging on energy consumption under different combinations of aging phases and driving modes. It is calibrated through full life-cycle testing, simulating different driving modes under various aging phases, and collecting energy consumption data. The associated data is obtained by fitting coefficients using the least squares method, which is typically used... , , , , The first subscript indicates the aging phase sequence number, and the second subscript indicates the driving mode sequence number. For example, when the first subscript is 1, it indicates the initial stage; when the first subscript is 2, it indicates the transition stage; when the first subscript is 3, it indicates the decline stage; and when the first subscript is 4, it indicates the steep decline stage. The second subscript indicates the driving mode sequence number: when the second subscript is 1, it indicates the smooth driving mode; when the second subscript is 2, it indicates the mild fluctuation driving mode; and when the second subscript is 3, it indicates the aggressive driving mode.

[0042] Indicates the amount of SOH decay, through The calculations are used to quantify the degree of battery aging.

[0043] The velocity-aging coupling function quantifies the nonlinear effect of average velocity on energy consumption under different combinations, and its expression is:

[0044] in This represents the average velocity of the segment. This indicates the pre-calibrated characteristic speed of the combination, which is determined by bench resistance and rolling resistance tests and decreases as the aging process progresses.

[0045] The sliding window Shannon entropy value for each driving mode is obtained by first processing the real-time vehicle speed data within each driving segment using a sliding window method, and then calculating it based on the vehicle speed distribution probability. The core purpose is to accurately quantify the uncertainty and complexity of vehicle speed changes within the segment, providing quantitative input for the energy consumption equation regarding the complexity of operating conditions. The specific method of sliding window processing is as follows: the sliding window duration is set to 5 seconds and the step size is 1 second. Based on the vehicle speed data collected at a frequency of 10Hz within the driving segment, starting from the beginning of the segment, 50 consecutive vehicle speed data points (corresponding to a duration of 5 seconds) are sequentially extracted to form a sliding window until the entire driving segment is covered. This ensures that the vehicle speed data within each window can reflect short-term driving state characteristics and achieve data continuity through step overlap.

[0046] For each sliding window, idling speeds (≤0.5km / h) are first removed. Then, the effective speed data is divided into eight equally spaced intervals based on the common driving speed of electric vehicles, 0-160km / h. Each interval spans 20km / h, i.e., [0,20) km / h, [20,40) km / h, ..., [140,160] km / h, ensuring coverage of speed ranges in all actual driving scenarios. The frequency of speed data falling within each interval in each window is counted, and the probability of the frequency of each interval relative to the total effective speed data in that window is calculated. This probability is substituted into the Shannon entropy calculation formula to obtain the Shannon entropy value of a single sliding window.

[0047] S5. Determine the combination corresponding to each driving segment, and calculate the energy consumption result of each driving segment according to the energy consumption equation corresponding to each combination. Generate the energy consumption result of the complete journey by weighting the energy consumption results of multiple driving segments.

[0048] For each driving segment, the Shannon entropy values ​​of all sliding windows contained therein are arithmetically averaged to obtain the corresponding sliding window Shannon entropy value. The larger the value, the more dispersed the vehicle speed distribution within the segment, and the more complex the driving conditions (such as frequent acceleration and deceleration in urban congestion, and alternating between low, medium and high speeds), and the higher the energy consumption level is usually. Conversely, the smaller the sliding window Shannon entropy value, the more concentrated the vehicle speed distribution, and the more stable the driving conditions (such as constant speed driving on highways), and the relatively lower the energy consumption level.

[0049] In some implementations, determining the combination corresponding to each driving segment specifically includes: Based on the battery health status data in the driving segment, determine the battery aging phase to which it belongs; The standardized feature vector composed of the vehicle driving state feature parameters of the driving segment is used to calculate the distance with all the determined driving mode cluster centers, and the segment is classified into the driving mode corresponding to the cluster center with the smallest distance. The determined battery aging phase and driving mode are used as a combined index and mapped to the corresponding matrix elements in the coupling matrix.

[0050] Furthermore, the corresponding values ​​in the driving segment are substituted into the corresponding energy consumption equation to obtain the energy consumption calculation result for that driving segment. The energy consumption calculation results of multiple driving segments are then weighted to generate the energy consumption result for the complete journey.

[0051] Furthermore, the proportion of the travel distance corresponding to each travel segment to the total distance of the complete journey is used as the weighting coefficient of that segment. The energy consumption results of each segment are multiplied by their corresponding weighting coefficients and then summed to obtain the energy consumption results of the complete journey.

[0052] Specifically, determining the battery aging phase of a driving segment requires using the current State of Health (SOH) value collected by the Battery Management System (BMS), the effective usage time T recorded by the onboard timing module's charge / discharge logs, and the total number of cycles N automatically counted by the BMS. These values ​​are then substituted into a preset aging comprehensive coefficient calculation formula to obtain the corresponding K value for that driving segment. The aging phase is then determined based on a quantification threshold: K ≥ 0.85 indicates the initial stage, 0.70 ≤ K < 0.85 indicates the transition stage, 0.55 ≤ K < 0.70 indicates the degradation stage, and K < 0.55 indicates the steep decline stage. Throughout the entire determination process, parameter acquisition and calculation are automatically completed by the onboard equipment.

[0053] The driving mode matching process requires extracting four parameters from the segment: speed standard deviation, acceleration mean, Shannon entropy, and acceleration / deceleration event frequency. These parameters are then processed using the Z-score standardization method described earlier. This involves calculating the global mean and standard deviation of each parameter to transform the original data into standardized data with a mean of 0 and a standard deviation of 1, eliminating the influence of dimensional differences on the matching results. The standardized four parameters are then combined in a fixed order to form a four-dimensional standardized feature vector corresponding to the driving segment. Next, the Euclidean distance between this standardized feature vector and all determined driving mode cluster centers is calculated. The driving segment is then classified into the driving mode corresponding to the cluster center with the smallest Euclidean distance, indicating that the driving characteristics of this segment best match the typical characteristics of that driving mode.

[0054] After determining the battery aging phase and driving mode, these two are used as a combined index and mapped to a pre-established battery aging phase-driving mode coupling matrix. Since the coupling matrix has the aging phase as the first dimension and the driving mode as the second dimension, each index combination uniquely corresponds to a cell in the matrix, and this cell is associated with a specific energy consumption equation. Therefore, this mapping can directly locate the energy consumption calculation model required for the current driving segment.

[0055] After obtaining the corresponding energy consumption equation, the required input parameters for the energy consumption equation are extracted from the monitoring data of the current driving segment, including: parameters used for calculation. The current SOH value, average speed v of the segment, and Shannon entropy H of the sliding window are used as parameters. These parameters are substituted into the energy consumption equation at the specified location, and the energy consumption calculation result for that driving segment is obtained through formula calculation.

[0056] To generate the energy consumption results for the entire trip, a weighted summation method based on the proportion of driving distance is adopted. First, the driving distance for each segment is calculated by integrating the effective vehicle speed data at a 10Hz sampling frequency within that segment over time, excluding invalid values ​​from idling. Then, the total driving distance for the entire trip is calculated, which is the sum of the driving distances of all segments. The weight coefficient for each segment is obtained by dividing its distance by the total driving distance of the entire trip. The sum of the weight coefficients for all segments is 1 to ensure the rationality of the weighting logic. Finally, the energy consumption calculation result for each segment is multiplied by its corresponding weight coefficient to obtain the weighted energy consumption value for that segment. These weighted energy consumption values ​​are then summed to obtain the final energy consumption result for the entire trip. This weighting method fully considers the differences in the proportion of different driving segments in the trip, avoiding deviations in energy consumption results caused by uneven distribution of segment duration or driving conditions, making the energy consumption monitoring for the entire trip more closely reflect actual driving conditions.

[0057] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for quantitative monitoring of energy consumption of electric vehicles, characterized in that, The monitoring method includes the following steps: The battery aging phase is classified according to the SOH value of the vehicle battery; A complete journey of an electric vehicle is divided into multiple driving segments with a certain duration. The vehicle driving state characteristic parameters of each driving segment are extracted from the segments. Cluster analysis is performed based on vehicle driving status characteristic parameters to obtain multiple different driving modes; Establish a battery aging phase-driving mode coupling matrix. Each element in the coupling matrix corresponds to a different combination of battery aging phase and driving mode, and establish a corresponding energy consumption equation based on each combination. The combination corresponding to each driving segment is determined, and the energy consumption result of each driving segment is calculated according to the energy consumption equation corresponding to each combination. The energy consumption results of multiple driving segments are weighted to generate the energy consumption result of the complete journey.

2. The method for quantitative monitoring of energy consumption of electric vehicles according to claim 1, characterized in that, The SOH value is compared with multiple predetermined aging phase threshold ranges to determine the current aging phase of the battery, wherein the aging phase includes multiple different aging stages based on the battery's health status.

3. The method for quantitative monitoring of energy consumption of electric vehicles according to claim 1, characterized in that, The vehicle driving state characteristic parameters include speed standard deviation, mean acceleration, Shannon entropy, and frequency of acceleration / rapid deceleration events.

4. The method for quantitative monitoring of energy consumption of electric vehicles according to claim 1, characterized in that, The complete journey is continuously divided into multiple time intervals using a preset fixed time length as the unit, where each time interval corresponds to a driving segment, and the driving segments are continuous in time and do not overlap.

5. The method for quantitative monitoring of energy consumption of electric vehicles according to claim 1, characterized in that, Cluster analysis is performed based on vehicle driving state characteristic parameters to obtain multiple different driving modes. Specifically, this includes generating corresponding feature vectors for all vehicle driving state characteristic parameters, using the distance between feature vectors as a similarity metric for cluster analysis, employing the XMeans algorithm which can automatically discover the number of clusters, and using the Bayesian information criterion as a guide during the cluster analysis process. The cluster centers of different clusters represent different driving modes. Multiple different driving modes are obtained through clustering, with each driving mode representing a driving state with the same energy consumption level.

6. The energy consumption quantitative monitoring method for electric vehicles according to claim 5, characterized in that, Establishing a battery aging phase-driving mode coupling matrix specifically includes: constructing a two-dimensional matrix group with multiple aging phases as the first dimension and multiple driving modes as the second dimension, wherein each matrix element in the matrix is ​​determined by the correspondence between a specific aging phase and a specific driving mode.

7. The energy consumption quantitative monitoring method for electric vehicles according to claim 6, characterized in that, For each cell in the matrix that is determined by a specific aging phase and a specific driving mode, an energy consumption equation is independently established. Some parameters in the energy consumption equation are determined by fitting the operating data collected from real vehicle tests using regression analysis, while the remaining parameters are calculated based on the SOH value and the average speed of the segment.

8. The method for quantitative monitoring of energy consumption of an electric vehicle according to claim 7, characterized in that, Determine the combination corresponding to each driving segment, specifically including: Based on the battery health status data in the driving segment, determine the battery aging phase to which it belongs; The standardized feature vector composed of the vehicle driving state feature parameters of the driving segment is used to calculate the distance with all the determined driving mode cluster centers, and the segment is classified into the driving mode corresponding to the cluster center with the smallest distance. The determined battery aging phase and driving mode are used as a combined index and mapped to the corresponding matrix elements in the coupling matrix.

9. The method for quantitative monitoring of energy consumption of an electric vehicle according to claim 8, characterized in that, The corresponding values ​​in the driving segment are substituted into the corresponding energy consumption equation to obtain the energy consumption calculation result for that driving segment. The energy consumption calculation results of multiple driving segments are weighted to generate the energy consumption result of the complete trip.

10. The method for quantitative monitoring of energy consumption of an electric vehicle according to claim 9, characterized in that, The proportion of the travel distance corresponding to each travel segment to the total distance of the complete journey is used as the weight coefficient of that segment. The energy consumption results of each segment are multiplied by their corresponding weight coefficients and then summed to obtain the energy consumption results of the complete journey.

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